| 摘要: |
| 缓解相对贫困是实现共同富裕的基本前提。构建包含心理健康、环境质量等要素的多维相对贫困指标体系，借鉴A-F贫困框架体系，利用中国家庭追踪调查（CFPS）数据，引入机器学习中的随机权神经网络（NNRW）法，精准测度并分解中国城乡间、区域间居民的多维相对贫困广度、深度和强度水平。研究发现：无论城乡间还是区域间，随着相对贫困维度的增加，多维相对贫困的广度、深度和强度指数均呈下降趋势，表明发生极端多维相对贫困的居民数量逐渐递减。同时，居民多维相对贫困指数呈“西高东低”态势，全国居民多维相对贫困水平大致与中部地区相当；农村居民的多维相对贫困水平显著高于城镇居民，且农村居民的多维相对贫困程度与西部地区相当；城镇居民的多维相对贫困水平大致与东部地区相当。此外，多维相对贫困指数分解结果显示，金融产品、生活环境、耐用品和人均纯收入等因素是城乡间、区域间居民发生相对贫困的主因，但是致贫主因对贫困广度、深度和强度的贡献率有所差别。研究结论为制定解决多维相对贫困长效机制提供了理论参考和政策依据。 |
| 关键词: 多维相对贫困 相对剥夺 随机权神经网络 环境质量 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Precision Measurement and Decomposition of Multidimensional Relative Poverty |
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CHE Si-fang1,21,2
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1.Institute for Chengdu-Chongqing Economic Zone Development, Chongqing Technology and Business University, Chongqing 400067, China;2.Chongqing Comprehensive Economic Research Institute, Chongqing 401120, China
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| Abstract: |
| Alleviating relative poverty is a fundamental prerequisite for achieving common prosperity. This study constructs a multidimensional relative poverty index system, incorporating factors such as mental health and environmental quality, and adopts the A-F poverty framework system. Utilizing data from the China Family Panel Studies (CFPS) and employing the neural network random weight (NNRW) method from machine learning, it precisely measures and decomposes the breadth, depth, and intensity levels of multidimensional relative poverty among urban-rural and regional residents in China. The research finds that regardless of urban-rural or regional disparities, as the dimensions of relative poverty increase, the breadth, depth, and intensity indices of multidimensional relative poverty all decrease, indicating a gradual reduction in the number of residents experiencing extreme multidimensional relative poverty. Meanwhile, residents’ multidimensional relative poverty indices exhibit a “high in the west and low in the east” trend, with the overall level of multidimensional relative poverty among residents roughly equivalent to that of the central regions. Rural residents’ multidimensional relative poverty levels are significantly higher than urban residents’, and rural residents’ multidimensional relative poverty levels are similar to those of the western regions; while urban residents’ multidimensional relative poverty levels are roughly equivalent to those of the eastern regions. Additionally, the decomposition results of the multidimensional relative poverty index show that factors such as financial products, living environment, durable goods, and per capita net income are the main reasons for relative poverty among urban-rural and regional residents, but the contribution rates of poverty determinants to the breadth, depth, and intensity differ. The research conclusions provide theoretical references and policy bases for formulating long-term mechanisms to address multidimensional relative poverty. |
| Key words: multidimensional relative poverty relative deprivation neural network random weight environmental quality |
